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_session-17-deployment-of-machine-learning-models-in-open-ran-networks.webp "Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \")
_unleashing-the-potential-of-private-5g-gxc39s-revolutionary-leap-in-connectivity-solutions.webp "evan kirstel")
⏲ 22 minutes 56 seconds 👁 7
_how-to-turn-on-5g-on-iphone-15.webp "Solve Your Tech")
_session-14-introduction-to-supervised-machine-learning-in-open-ran.webp "Welcome to Session 14 of our Open RAN series! In this session, we'll introduce supervised machine learning and its application in designing intelligent systems for Open RAN.<br/><br/><br/>Understanding Supervised Machine Learning:<br/>Supervised machine learning is a type of machine learning where the algorithm learns from labeled data. It involves training a model on a dataset that contains input-output pairs, where the input is the data and the output is the corresponding label or target variable. The algorithm learns to map inputs to outputs by finding patterns in the data. In Open RAN, supervised learning can be used for tasks such as predicting network performance based on historical data.<br/><br/>Types of Supervised Machine Learning:<br/>There are two main types of supervised machine learning: classification and regression. In classification, the algorithm learns to categorize data into predefined classes or categories. For example, it can classify network traffic into different application types (e.g., video streaming, web browsing). Regression, on the other hand, involves predicting continuous values or quantities. It is used when the output variable is a real or continuous value, such as predicting the signal strength of a network connection.<br/><br/>Binary and Multi-Class Classification:<br/>Binary classification involves categorizing data into two classes or categories. For example, it can be used to classify network traffic as either malicious or benign. Multi-class classification, on the other hand, involves categorizing data into more than two classes. It can be used to classify network traffic into multiple application types (e.g., video streaming, social media, email).<br/><br/>Regression in Machine Learning:<br/>Regression is a supervised learning technique used for predicting continuous values or quantities. It involves fitting a mathematical model to the data, which can then be used to make predictions. In Open RAN, regression can be used for tasks such as predicting network latency, throughput, or coverage based on various input variables such as network parameters, traffic patterns, and environmental conditions.<br/><br/>Subscribe to \")
_season-3-episode-23-how-nist-is-helping-to-secure-5g-infrastructure.webp "GovCIO Media u0026 Research")
_introduction-to-5g-internet-speed-lecture-05.webp "Computing Tech")
_happy-new-year-5g-speed-purula-dj-song-nonstop.webp "<br/>Description<br/><br/>Hey friends This Is Lakshman Welcome To Our daily motion Channel. Here I presenting You All Types Of DJ Songs. Regular update my channel<br/> Like Comments And Share Our Videos Thank U. I Hope You Enjoying Our Videos.<br/><br/>Please Subscribe Our Channel For More DJ Songs updates<br/>")
_introduction-to-5g-internet-speed-lecture-03.webp "Computing Tech")
_what-is-5g-enter-into-the-world-of-modern-science-and-technology.webp "Edu Genius")
_session-23-multi-access-edge-computing-mec-in-open-ran-oran-explained-for-beginners.webp "Welcome to Session 23! In this video, we'll dive into the world of Multi-Access Edge Computing (MEC) within the framework of Open RAN (ORAN). This beginner-friendly session breaks down the concepts, providing an easy-to-understand overview with real-world examples. We discuss the key benefits, potential challenges, and various use cases of MEC in ORAN. Learn why MEC is crucial for the future of mobile networks and how it can revolutionize connectivity and performance.<br/><br/>Key Concepts :<br/>* Introduction to MEC and ORAN<br/>* Key advantages of MEC in ORAN<br/>* Common challenges and disadvantages<br/>* Practical use cases of MEC in ORAN<br/>* The importance of MEC in modern network architecture<br/><br/><br/>Welcome to Session 23!<br/>Hello everyone, and welcome to Session 23 of our series. Today, we will explore the fascinating topic of Multi-Access Edge Computing (MEC) in the context of Open RAN (ORAN). This session is designed for beginners, so we'll start with the basics and build up to more detailed insights.<br/><br/>Introduction to MEC and ORAN<br/>Multi-Access Edge Computing (MEC) is an innovative technology that brings computation and data storage closer to the end user, significantly enhancing the performance and efficiency of applications and services. Open RAN (ORAN) is a new approach to building mobile networks using open and interoperable interfaces. By combining MEC with ORAN, we create a powerful synergy that transforms how data is processed and transmitted across networks.<br/><br/>Key Advantages of MEC in ORAN<br/>* Reduced Latency: Processing data closer to the source drastically reduces latency, which is critical for applications such as online gaming, video streaming, and virtual reality.<br/>* Improved Network Efficiency: MEC offloads traffic from the core network, resulting in more efficient use of network resources and better overall performance.<br/>* Enhanced Security: Local data processing offers improved security and privacy controls, reducing the risk of data breaches.<br/><br/>Common Challenges and Disadvantages<br/>* Complexity: Implementing MEC in an ORAN environment can be complex, requiring careful planning and integration.<br/>* Cost: Initial deployment costs can be high, but the long-term benefits often justify the investment.<br/><br/>Practical Use Cases of MEC in ORAN<br/>* Smart Cities: MEC enables real-time data processing for traffic management, public safety, and environmental monitoring.<br/>* Healthcare: Supports advanced telemedicine applications by providing low-latency, high-reliability connections for remote surgeries and patient monitoring.<br/>* Industrial IoT: Facilitates real-time analytics and automation in manufacturing, improving efficiency and reducing downtime.<br/><br/>Subscribe to \")
⏲ 4:48 👁 15K
_tech-tuesday-with-cto-tom-what-is-5g.webp "CTIA Everything Wireless")
_5g-architecture-reference-model-for-non-3gpp-access.webp "TELCOMA Global")
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